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Record W2729317006 · doi:10.1097/prs.0000000000003476

Evidence-Based Medicine: Surgical Management of Flexor Tendon Lacerations

2017· article· en· W2729317006 on OpenAlexaff
Robin N. Kamal, Jeffrey Yao

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsHand and Upper Limb Clinic
Fundersnot available
KeywordsMedicineTendonRehabilitationSurgeryPhysical examinationConcomitantProtocol (science)Physical medicine and rehabilitationPhysical therapyAlternative medicinePathology

Abstract

fetched live from OpenAlex

LEARNING OBJECTIVES: After reading this article, the participant should be able to: 1. Accurately diagnose a flexor tendon injury. 2. Develop a surgical approach with regard to timing, tendon repair technique, and rehabilitation protocol. 3. List the potential complications following tendon repair. SUMMARY: Flexor tendon lacerations are complex injuries that require a thorough history and physical examination for accurate diagnosis and management. Knowledge of operative approaches and potential concomitant injuries allows the surgeon to be prepared for various findings during exploration. Understanding the biomechanical principles behind tendon lacerations and repair techniques aids the surgeon in selecting the optimal repair technique and postoperative rehabilitation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.315
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2017
Admission routes1
Has abstractyes

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